Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

438
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
438
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

359
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
359
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

333
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
333
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.5K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.5K
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

725
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
725
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.3K
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
1.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

International Union of Basic and Clinical Pharmacology. CXXII. Applying an objective evaluation to the status of class A orphan G protein-coupled receptors.

Pharmacological reviews·2026
Same author

Evaluating the effects of sacubitril/valsartan with ST-elevated myocardial infarction: a systematic review and meta-analysis.

Annals of medicine and surgery (2012)·2026
Same author

Keying Into Cognition: Temporal Smoothing of Smartphone Typing Behaviors for Passive Assessment of Processing Speed and Executive Function in Individuals With Mood Disorders.

Cognitive computation·2026
Same author

The Concise Guide to PHARMACOLOGY 2025/26: G protein-coupled receptors.

British journal of pharmacology·2025
Same author

Unobtrusive inference of diurnal rhythms from smartphone data.

NPJ digital medicine·2025
Same author

G Protein-Coupled Receptor Signaling in CNS (Re)Myelination.

Journal of neurochemistry·2025

Related Experiment Video

Updated: Apr 27, 2026

High-Throughput Metabolic Profiling for Model Refinements of Microalgae
11:07

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

3.6K

Parameter discovery in stochastic biological models using simulated annealing and statistical model checking.

Faraz Hussain1, Sumit K Jha1, Susmit Jha2

  • 1Computer Science Department, University of Central Florida, Orlando, FL 32816, USA.

International Journal of Bioinformatics Research and Applications
|July 4, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a novel algorithm for discovering parameters in stochastic biochemical models. The method aids in developing artificial pancreata by efficiently learning model parameters from experimental data.

Keywords:
CPSCUDASPRTartificial pancreatabehavioural specificationsbiochemical systemsbioinformaticsbiomedical devicescomputational systems biologycyber–physical systemsglucose–insulin modelmachine learningparameter discoveryparameter synthesisprobabilistic verificationsimulated annealingstatistical hypothesis testingstatistical model checkingstochastic modellingtemporal logic

More Related Videos

Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function
05:57

Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function

Published on: April 26, 2024

1.0K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

1.7K

Related Experiment Videos

Last Updated: Apr 27, 2026

High-Throughput Metabolic Profiling for Model Refinements of Microalgae
11:07

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

3.6K
Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function
05:57

Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function

Published on: April 26, 2024

1.0K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

1.7K

Area of Science:

  • Computational systems biology
  • Biochemical modeling
  • Algorithm development

Background:

  • Stochastic models are crucial for understanding biochemical systems.
  • Parameter estimation in these models from experimental data is a significant computational challenge.

Purpose of the Study:

  • To present a new algorithmic approach for discovering unknown parameters in stochastic models.
  • To apply this technique to a model of glucose and insulin metabolism for artificial pancreas development.

Main Methods:

  • The algorithm integrates simulated annealing, sequential hypothesis testing, and statistical model checking.
  • A parallel CUDA-based implementation was developed for efficient parameter synthesis.

Main Results:

  • The developed algorithm effectively learns parameters for stochastic models.
  • Its application to the artificial pancreas model demonstrates successful in-silico validation.

Conclusions:

  • The novel parameter discovery algorithm offers a robust solution for computational systems biology.
  • This work facilitates the development and validation of complex biological models, such as those for artificial pancreata.